边方向与网络结构
Edge direction and the structure of networks
摘要：
有向网络是无处不在的，并且是表示具有不对称相互作用的复杂系统所必需的--从食物网到万维网。尽管边缘方向对于检测局部和社区结构很重要，但在研究网络中的一种基本类型的全局多样性时，它被忽视了：具有相似边缘数量的节点连接的趋势。这种趋势称为同配性，会影响现实世界网络的关键结构和动态属性，例如容错性或传染病传播。在这里，我们证明了边缘方向有深远的影响，反磁性。我们定义了一组四个有向重复性的措施，并分配随机网络的比较统计意义。我们将这些措施应用到三个网络类-在线/社交网络，食物网，词邻接网络。我们的措施（i）揭示了每个类别的共同模式，（ii）以前被分类在一起的独立网络，（iii）暴露了几个现有理论模型的局限性。我们拒绝将有向网络的标准分类为纯粹的排斥或排斥。许多显示类特定的混合，可能反映功能或历史的限制，偶然性，和力量引导系统的演变。
英文摘要：
Directed networks are ubiquitous and are necessary to represent complex systems with asymmetric interactions---from food webs to the World Wide Web. Despite the importance of edge direction for detecting local and community structure, it has been disregarded in studying a basic type of global diversity in networks: the tendency of nodes with similar numbers of edges to connect. This tendency, called assortativity, affects crucial structural and dynamic properties of real-world networks, such as error tolerance or epidemic spreading. Here we demonstrate that edge direction has profound effects on assortativity. We define a set of four directed assortativity measures and assign statistical significance by comparison to randomized networks. We apply these measures to three network classes---online/social networks, food webs, and word-adjacency networks. Our measures (i) reveal patterns common to each class, (ii) separate networks that have been previously classified together, and (iii) expose limitations of several existing theoretical models. We reject the standard classification of directed networks as purely assortative or disassortative. Many display a class-specific mixture, likely reflecting functional or historical constraints, contingencies, and forces guiding the system's evolution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
